An abnormal irregularity cause identifying device includes a process data acquisition unit that reads, from a storage device storing process data and each associated with a management number of a processing target, the pieces of process data, an abnormality determination unit that continuously calculates an abnormality degree representing an extent of an irregularity of process data of the pieces of process data read by the process data acquisition unit, and a cause diagnosis unit that determines, for each of the pieces of process data and corresponding to the management number of the processing target, whether the abnormality degree calculated by the abnormality determination unit satisfies a predetermined criterion by using causal relation information defining a combination between a cause and the irregularity, which appears as an influence resulting from the cause, of the process data.

A system for monitoring potential failure in a machine or a component thereof, the system including: at least one optical sensor configured to be fixed on or in vicinity of the machine or the component thereof, at least one processor in communication with the sensor, the processor being executable to: receive signals from the at least one optical sensor, obtain data associated with characteristics of at least one mode of failure of the machine or the component thereof, identify at least one change in the received signals, for an identified change in the received signals, apply the at least one identified change to an algorithm configured to analyze the identified change in the received signals and to classify whether the identified change in the received signals is associated with a mode of failure of the machine or the component thereof, thereby labeling the identified change as a fault, based, at least in part, on the obtained data, and for an identified change is classified as being associated with a mode of failure, outputting a signal indicative of the identified change associated with the mode of failure.

The application is directed to systems and methods of performing anomaly detection, predictive maintenance, and anomaly correction in an amusement park experience. A method may include receiving, via a sensor network, multiple layers of first sensor data indicative of characteristics of the experience and generating a profile of the experience based on the first sensor data, wherein the profile includes a baseline and a threshold. The method may also include receiving second sensor data and third sensor data via the sensor network, determining, in response to identifying characteristics of the second sensor data that deviate from the baseline but do not exceed the threshold, that the experience is operating properly, and performing a particular corrective action in response to identifying characteristics of the third sensor data that deviate from the baseline and exceed the threshold.

A system including a deep learning processor receives one or more control signals from one or more of a factory's process, equipment and control (P/E/C) systems during a manufacturing process. The processor generates expected response data and expected behavioral pattern data for the control signals. The processor receives production response data from the one or more of the factory's P/E/C systems and generates production behavioral pattern data for the production response data. The process compares at least one of: the production response data to the expected response data, and the production behavioral pattern data to the expected behavioral pattern data to detect anomalous activity. As a result of detecting anomalous activity, the processor performs one or more operations to provide notice or cause one or more of the factory's P/E/C systems to address the anomalous activity.

A method of analyzing a specimen includes detecting a specimen integrity error in the specimen; capturing an image of the specimen; sending the image of the specimen to a customer support center at a remote location; analyzing the image of the specimen at the customer support center; and determining a cause of the specimen integrity error in response to analyzing the image of the specimen. Diagnostic analyzers and diagnostic systems are also disclosed.

A power system that uses machine learning algorithms to solve various problems related to the delivery of power on integrated circuit systems is provided. The power system may process data on a platform near the target integrated circuit or off-platform in a cloud so that the machine learning algorithms can extract information from the data, process and analyze the data, and perform suitable action based on the analysis results. Applying machine learning to integrated circuit power delivery may involve the application of algorithms such as anomaly detection, load prediction, regression, and classification. Operated in this way, the power system may be provided with improved voltage/frequency scaling capabilities, security, and power efficiency.

A system includes a first most probable cause (MPC) module, a second MPC module, and an integrated MPC module. The first MPC module is configured to determine a first most probable cause of an issue on a vehicle based on at least one service procedure for the vehicle. The second MPC module is configured to determine a second most probable cause of the issue based on repair data for other vehicles. The integrated MPC module is configured to determine an integrated most probable cause of the issue based on the first and second most probable causes.

A system includes a diagnostic step module and an optimal diagnostic procedure module. The diagnostic step module is configured to identify diagnostic steps of a service procedure to be performed to diagnose a root cause of a fault on a vehicle based on a diagnostic trouble code identifying the fault, and identify a cost associated with performing each of the diagnostic steps. The optimal diagnostic procedure module is configured to determine an order in which to perform the diagnostic steps that minimizes the cost of diagnosing the fault, and output an optimal diagnostic procedure that indicates the diagnostic steps and the order in which to perform the diagnostic steps.

An information processing system according to an embodiment includes edge terminals, an information processing apparatus, and one or more service providing apparatuses. The edge terminals each transmit, to the information processing apparatus, monitoring data indicating a state of a target system to be analyzed. The information processing apparatus performs an analysis process by using an analysis mod& that inputs an input value including the monitoring data transmitted from the edge terminals and outputs an output value of a value function. The analysis process is a process to obtain the output value in response to the input value, The information processing apparatus transmits, to the service providing apparatuses, information indicating an analysis result of the analysis process. The service providing apparatuses each output information obtained by visualizing the analysis result on the basis of information indicating the analysis result.

An abnormality diagnosis apparatus includes: a friction identification unit that calculates a friction parameter that is a parameter used for calculation of frictional force of a power transmission mechanism connected to a motor; a model torque calculation unit that calculates model torque by performing a process of calculating an estimated value of torque of the motor by using a set value calculated in advance and the friction parameter; and an abnormality determination unit that diagnoses whether the power transmission mechanism is abnormal, on the basis of a result of comparison between the model torque and a motor torque detected by a motor torque detection unit.